Recent advancements in sensor and wireless communication technologies have opened up many opportunities to acquire biomedical signals at a very low cost. Many sensors are compact enough to be worn by the subjects, and can continuously gather data for a prolonged period. These technologies have quickly found their natural applications in health care in the form of eHealth and telemedical systems.
Most of the early eHealth systems focus on remote monitoring and abnormality detection. While providing a low-cost and convenient way for health care personnel to monitor the well-being of patients, the majority of these systems are essentially infrastructures for data collection and storage. One of the main goals of the next generation of eHealth systems is to mine and analyze the sensor data for so-called “precursor patterns.” These patterns are highly correlated to an ensuing medical condition or clinical episode. Using precursor patterns to predict medical events will be important for the next generation of eHealth systems.
Thus far, there have been several studies dedicated to identifying precursor patterns with a varying degree of success. However, one common problem with these studies is the requirement of domain-specific knowledge to develop their discovery algorithms. Also, most of the algorithms require prior knowledge of the duration of the precursor patterns, as well as the time the patterns are likely to occur relative to the clinical episode. Consequently, it is often impossible to apply a given precursor pattern discovery algorithm to a different medical condition without significant modification or degradation in performance.